Robust Monotone Submodular Function Maximization

TL;DR

This paper introduces new approximation algorithms for robust monotone submodular maximization under adversarial element removal, achieving constant-factor guarantees and extending to general independence systems.

Contribution

It provides the first constant-factor approximation algorithms for robust monotone submodular maximization, including deterministic and randomized methods, and extends results to independence systems.

Findings

Deterministic $(1-1/e)-1/\Theta(m)$ approximation for $ au=1$.

Randomized $(1-1/e)-\epsilon$ approximation for constant $ au$ and small $\epsilon$.

A practical 0.387 approximation algorithm for small $\tau$.

Abstract

We consider a robust formulation, introduced by Krause et al. (2008), of the classical cardinality constrained monotone submodular function maximization problem, and give the first constant factor approximation results. The robustness considered is w.r.t. adversarial removal of up to $\tau$ elements from the chosen set. For the fundamental case of $\tau=1$, we give a deterministic $(1-1/e)-1/\Theta(m)$ approximation algorithm, where $m$ is an input parameter and number of queries scale as $O(n^{m+1})$. In the process, we develop a deterministic $(1-1/e)-1/\Theta(m)$ approximate greedy algorithm for bi-objective maximization of (two) monotone submodular functions. Generalizing the ideas and using a result from Chekuri et al. (2010), we show a randomized $(1-1/e)-\epsilon$ approximation for constant $\tau$ and $\epsilon\leq \frac{1}{\tilde{\Omega}(\tau)}$, making $O(n^{1/\epsilon^3})$ queries. Further, for $\tau\ll \sqrt{k}$, we give a fast and practical 0.387 algorithm. Finally, we also give a black box result result for the much more general setting of robust maximization subject to an Independence System.